Review on dynamic analysis of road pavements under moving vehicles and plane strain conditions

This paper reviews works on the dynamic analysis of flexible and rigid pavements under moving vehicles on the basis of continuum-based plane strain models and linear theories.The purpose of this review is to provide in-formation about the existing works on the subject,critically discuss them and make suggestions for further research.The reviewed papers are presented on the basis of the various models for pavement-vehicle systems and the various methods for dynamically analyzing these systems.Flexible pavements are modeled by a homogeneous or layered half-plane with isotropic or anisotropic and linear elastic,viscoelastic or poroelastic material behavior.Rigid pavements are modeled by a beam or plate on a homogeneous or layered half-plane with material properties like the ones for flexible pavements.The vehicles are modeled as concentrated or distributed over a finite area loads moving with constant or time dependent speed.The above pavement-vehicle models are dynamically analyzed by analytical,analytical/numerical or purely numerical methods working in the time or frequency domain.Representative examples are presented to illustrate the models and methods of analysis,demonstrate their merits and assess the effects of the various parameters on pavement response.The paper closes with con-clusions and suggestions for further research in the area.The significance of this research effort has to do with the presentation of the existing literature on the subject in a critical and easy to understand way with the aid of representative examples and the identification of new research areas.

Predicting the International Roughness Index of JPCP and CRCP Rigid Pavement:A Random Forest(RF)Model Hybridized with Modified Beetle Antennae Search(MBAS)for Higher Accuracy

To improve the prediction accuracy of the International Roughness Index(IRI)of Jointed PlainConcrete Pavements(JPCP)and Continuously Reinforced Concrete Pavements(CRCP),a machine learning approach is developed in this study for the modelling,combining an improved Beetle Antennae Search(MBAS)algorithm and Random Forest(RF)model.The 10-fold cross-validation was applied to verify the reliability and accuracy of the model proposed in this study.The importance scores of all input variables on the IRI of JPCP and CRCP were analysed as well.The results by the comparative analysis showed the prediction accuracy of the IRI of the newly developed MBAS and RF hybrid machine learning model(RF-MBAS)in this study is higher,indicated by the RMSE and R values of 0.2732 and 0.9476 for the JPCP as well as the RMSE and R values of 0.1863 and 0.9182 for the CRCP.The accuracy of this obtained result far exceeds that of the IRI prediction model used in the traditional Mechanistic-Empirical Pavement Design Guide(MEPDG),indicating the great potential of this developed model.The importance analysis showed that the IRI of JPCP and CRCP was proportional to the corresponding input variables in this study,including the total joint faulting cumulated per KM(TFAULT),percent subgrade material passing the 0.075-mm Sieve(P_(200))and pavement surface area with flexible and rigid patching(all Severities)(PATCH)which scored higher.

Development and optimization of object detection technology in pavement engineering: A literature review

Due to the rapid advancement of the transportation industry and the continual increase in pavement infrastructure,it is difficult to keep up with the huge road maintenance task by relying only on the traditional manual detection method.Intelligent pavement detection technology with deep learning techniques is available for the research and industry areas by the gradual development of computer vision technology.Due to the different characteristics of pavement distress and the uncertainty of the external environment,this kind of object detection technology for distress classification and location still faces great challenges.This paper discusses the development of object detection technology and analyzes classical convolutional neural network(CNN)architecture.In addition to the one-stage and two-stage object detection frameworks,object detection without anchor frames is introduced,which is divided according to whether the anchor box is used or not.This paper also introduces attention mechanisms based on convolutional neural networks and emphasizes the performance of these mechanisms to further enhance the accuracy of object recognition.Lightweight network architecture is introduced for mobile and industrial deployment.Since stereo cameras and sensors are rapidly developed,a detailed summary of three-dimensional object detection algorithms is also provided.While reviewing the history of the development of object detection,the scope of this review is not only limited to the area of pavement crack detection but also guidance for researchers in related fields is shared.

Flexible pavement longitudinal joint quality evaluation using non-destructive testing

Longitudinal joint construction quality is critical to the life of flexible pavements.Maintaining deteriorated longitudinal joints has become a challenge for many highway agencies.Improving the joint's quality through better compaction during construction can help achieve flexible pavements with longer service lives and less maintenance.Current quality control(QC)and quality assurance(QA)plans provide limited coverage.Consequently,the risk of missing areas with poor joint compaction is significant.A density profiling system(DPS)is a non-destructive alternative to conventional destructive evaluation methods.It can provide quick and continuous real-time coverage of the compaction during construction in dielectrics.The paper presents several case studies comparing various types of longitudinal joints and demonstrating the use of DPS to evaluate the joint's compaction quality.The paper shows that dielectric measurements can provide valuable insight into the ability of various construction techniques to achieve adequate levels of compaction at the longitudinal joint.The paper proposes a dielectric-based longitudinal joint quality index(LJQI)to evaluate the relative compaction of the joint during construction.It also shows that adopting DPS for assessing the compaction of longitudinal joints can minimize the risk of agencies accepting poorly constructed joints,identify locations of poor quality during construction,and achieve better-performing flexible pavements.

A systematic review of rigid-flexible composite pavement

Rigid-flexible composite pavement has gained significant popularity in recent decades.This paper provides a comprehensive review of the research progress concerning rigid-flexible composite pavement,aiming to promote its application and address key issues while identifying future directions.The design theory and methodology of rigid-flexible composite pavement are discussed,followed by a description of its structural and mechanical behavior characteristics.The load stress,temperature stress,and their interactive effects between the asphalt layer and the rigid base were analyzed.It is clarified that the asphalt layer serves a dual role as both a“functional layer”and a“structural layer”.Typical distresses of rigid-flexible composite pavement,which primarily occur in the asphalt layer,were discussed.These distresses include reflective cracking,top-down cracking,rutting,and compressive-shear failure.Generally,the integrity of the rigid base and the interlaminar bonding conditions significantly impact the performance and distress of the asphalt layer.The technology for enhancing the performance of rigid-flexible composite pavement is summarized in three aspects:asphalt layer properties,rigid base integrity,and interlaminar bonding condition.The study concludes that developing high-performance pavement materials based on their structural behaviors is an effective approach to improve the performance and durability of rigid-flexible composite pavement.The integrated design of structure and materials represents the future direction of road design.

Analysis of Historic Pavement and Bridge Conditions and Alternative Funding Levels for the Michigan Department of Transportation

In 1997 the Michigan Department of Transportation (MDOT) established an ambitious set of condition targets for its pavements and bridges, and the Department received increased revenue from a 4-cent-per-gallon increase in the state motor fuels tax to help meet its targets. However, over time, actual revenue was less than both what was initially estimated as needed to meet the targets and what was projected from the tax increase. Consequently, actual conditions were projected to fall short of the target levels, so the department issued bonds to address the shortfall through 2012. To support deliberations on future funding, in 2013 MDOT performed an analysis of historic conditions to determine what additional fuel tax revenues would have been required beginning in 1997 to: replace bond revenues used to fund pavement and bridge projects from 1997 to 2012;and enable MDOT to meet its condition targets. The analysis was performed using data on actual pavement and bridge funding and conditions;as well as predicted funding and conditions for different hypothetical increases in fuel taxes. The analysis concluded that, in addition to the actual increase of 4 cents per gallon, a fuel tax increase of another 10 cents per gallon would have been required in 1997 to replace bond revenue used for pavement and bridges and allow MDOT to meet its condition targets. The analysis results were used to help inform the discussion of Michigan’s target asset conditions and funding, and demonstrate application of MDOT’s pavement and bridge management systems for performing historic analyses.

Analysis of Roadbed Pavement Designs for Highway Reconstruction and Upgrading

This paper focuses on the route and roadbed pavement design in highway reconstruction and upgrading projects.It discusses the importance of project design for highway reconstruction and upgrading,highlighting key aspects of route design and roadbed pavement design.The analysis reveals that the main design considerations in these projects include controlling factors of route reconstruction,expansion,and upgrading,as well as route plan design and longitudinal section design combined with roadbed pavement.In roadbed pavement design,it is crucial for designers to thoroughly collect existing data and make reasonable use of the current roadbed and pavement to develop a comprehensive design scheme.This analysis aims to provide a reference for the reasonable design of such projects.

A systematic review of steel bridge deck pavement in China

As an important part of steel bridge deck,the engineering quality and service condition of steel bridge deck pavement(SBDP)directly affects the capacity and operational efficiency of the bridge.This paper reviews the history of the development of SBDP in China over the past 20 years from the exploration stage,rapid development stage and prosperity stage.The development and application of SBDP at different stages are discussed in terms of materials,structure,design,performance evaluation,maintenance and rehabilitation,respectively.The advantages and disadvantages of different pavement materials and structures,and the application of different research methods are summarized.The review shows that the improvement of pavement materials and structures and the development of new materials should be further studied on the multi-scale to enhance the durability of pavement materials,so as to extend the service life of pavements.The design method of SBDP related to the synergistic effect of vehicle,pavement and bridge should be established,and the design concept and method standard of rigid base pavement structure should be improved and formulate a complete design standard.In addition,multi-disease intelligent identification system and equipment should be studied to track the entire course of disease development in real time.And it is necessary to develop appropriate algorithms to select and classify the complex data of disease and maintenance history.

Multi-objective optimization in highway pavement maintenance and rehabilitation project selection and scheduling:A state-of-the-art review

The motivation for cost-effective management of highway pavements is evidenced not only by the massive expenditures associated with these activities at a national level but also by the consequences of poor pavement condition on road users.This paper presents a state-of-the-art review of multi-objective optimization(MOO)problems that have been formulated and solution techniques that have been used in selecting and scheduling highway pavement rehabilitation and maintenance activities.First,the paper presents a taxonomy and hierarchy for these activities,the role of funding sources,and levels of jurisdiction.The paper then describes how three different decision mechanisms have been used in past research and practice for project selection and scheduling(historical practices,expert opinion,and explicit mathematical optimization)and identifies the pros and cons of each mechanism.The paper then focuses on the optimization mechanism and presents the types of optimization problems,formulations,and objectives that have been used in the literature.Next,the paper examines various solution algorithms and discusses issues related to their implementation.Finally,the paper identifies some barriers to implementing multi-objective optimization in selecting and scheduling highway pavement rehabilitation and maintenance activities,and makes recommendations to overcome some of these barriers.

Predicting pavement condition index based on the utilization of machine learning techniques:A case study

Pavement management systems(PMS)are used by transportation government agencies to promote sustainable development and to keep road pavement conditions above the minimum performance levels at a reasonable cost.To accomplish this objective,the pavement condition is monitored to predict deterioration and determine the need for maintenance or rehabilitation at the appropriate time.The pavement condition index(PCI)is a commonly usedmetric to evaluate the pavement's performance.This research aims to create and evaluate prediction models for PCI values using multiple linear regression(MLR),artificial neural networks(ANN),and fuzzy logic inference(FIS)models for flexible pavement sections.The authors collected field data spans for 2018 and 2021.Eight pavement distress factors were considered inputs for predicting PCI values,such as rutting,fatigue cracking,block cracking,longitudinal cracking,transverse cracking,patching,potholes,and delamination.This study evaluates the performance of the three techniques based on the coefficient of determination,root mean squared error(RMSE),and mean absolute error(MAE).The results show that the R2 values of the ANN models increased by 51.32%,2.02%,36.55%,and 3.02%compared toMLR and FIS(2018 and 2021).The error in the PCI values predicted by the ANNmodel was significantly lower than the errors in the prediction by the FIS and MLR models.

The mechanisms,evaluation and estimation of anti-skid performance of snowy and icy pavement:A review

The anti-skid performance of snowy and icy pavements is a popular research topic among road workers.Snow and ice are pollutants on a road surface.They significantly reduce the skid resistance of pavements,and thus,cause traffic accidents.Pertinent research progress on the skid resistance of snowy and icy pavements was reviewed and summarized in this work.The formation and classification of snowy and icy pavements were described on the basis of the state of snow and ice.The friction mechanisms between tires and snowy and icy pavements were revealed.Measurement methods and their applicability to the skid resistance of snowy and icy pavements were summarized.Factors that affect the skid resistance of pavements were discussed from the perspectives of pavement,environment,and vehicle.In addition,models of snowy and icy pavement resistance were classified into experience,mechanical,and numerical models.The advantages and disadvantages of these models were then compared and analyzed.Some suggestions regarding snowy and icy pavements were presented in accordance with the aforementioned information,including the development of efficient testing tools,the quantification of skid resistance under the coupling effects of multiple factors,the establishment of unified evaluation standards,and the development of more effective skid resistance models.

Pavement Cracks Coupled With Shadows:A New Shadow-Crack Dataset and A Shadow-Removal-Oriented Crack Detection Approach

Automatic pavement crack detection is a critical task for maintaining the pavement stability and driving safety.The task is challenging because the shadows on the pavement may have similar intensity with the crack,which interfere with the crack detection performance.Till to the present,there still lacks efficient algorithm models and training datasets to deal with the interference brought by the shadows.To fill in the gap,we made several contributions as follows.First,we proposed a new pavement shadow and crack dataset,which contains a variety of shadow and pavement pixel size combinations.It also covers all common cracks(linear cracks and network cracks),placing higher demands on crack detection methods.Second,we designed a two-step shadow-removal-oriented crack detection approach:SROCD,which improves the performance of the algorithm by first removing the shadow and then detecting it.In addition to shadows,the method can cope with other noise disturbances.Third,we explored the mechanism of how shadows affect crack detection.Based on this mechanism,we propose a data augmentation method based on the difference in brightness values,which can adapt to brightness changes caused by seasonal and weather changes.Finally,we introduced a residual feature augmentation algorithm to detect small cracks that can predict sudden disasters,and the algorithm improves the performance of the model overall.We compare our method with the state-of-the-art methods on existing pavement crack datasets and the shadow-crack dataset,and the experimental results demonstrate the superiority of our method.

Toughness improvement mechanism and evaluation of cement concrete for road pavement:A review

Traditional cement concrete has the disadvantages of low tensile strength,poor toughness,and rapid development of cracks while cracking,which causes a significantly negative influence on the safety and durability of concrete road pavement.This paper presents a state-of-the-art review of toughness improvement mechanisms and evaluation methods of cement concrete for road pavement.The review indicates that(i)The performance of concrete material depends on its material composition and internal structure.Aggregate size,cement properties and admixtures are the main factors of concrete toughness.(ii)The incorporation of rubber or fiber in pavement concrete improves the toughness of concrete materials.However,these additions must be maintained within a reasonable range.The amount of rubber and fiber are encouraged not more than 30%of the volume of fine aggregate and 2%of the volume of concrete,respectively.(iii)The toughness of pavement concrete material includes the toughness regarding bending,impact and fracture.The toughness of cement concrete for highway and municipal pavement is generally evaluated by bending and fracture toughness,while the toughness of airfield pavement concrete is more focused on impact toughness.(iv)The toughening measures of cement concrete for road pavement are mainly mixed with rubber or fiber,while these two materials have their defects,and the application of hightoughness cement concrete in the actual road still faces many challenges.For example,the synergistic effect of rubber and fiber,the development and application of new flexible admixtures,and the formulation of the toughness index of pavement cement concrete materials need further research.

Do aeolian deposits and sand encroachment intensity shape patterns of vegetation diversity and plant functional traits in desert pavements?

The effects of sand encroachment on composition,diversity,and functional patterns of vegetation in drylands are rarely studied,and yet addressing these aspects is important to deepen our understanding of the biodiversity conservation.This study aimed to investigate the effect of sand encroachment on plant functional biodiversity of desert pavements(gravel deserts)in the Sahara Desert of Algeria.Plants were sampled and analyzed in three desert pavements with different levels of sand encroachment(LSE)and quantity of aeolian deposits(low,LLSE;medium,MLSE;and high,HLSE).Within the sample-plot area(100 m^(2)),density of every plant species was identified and total vegetation cover was determined.Plant taxonomic and functional diversity were analyzed and compared between LSE.Result showed that 19 plant species in desert pavements were classified into 18 genera and 13 families.Asteraceae and Poaceae were the most important families.The species Anabasis articulata(Forssk)Moq.characterized LLSE desert pavements with 11 species,whereas Thymelaea microphylla Coss.&Durieu ex Meisn.and Calobota saharae(C&D)Boatwr.&van Wyk were dominant species of desert pavements with MLSE(14 species)and HLSE(10 species),respectively.The highest values of species richness and biodiversity were recorded in desert pavements with MLSE,while low values of these ecological parameters were obtained in desert pavements with HLSE.Desert pavements with LLSE were characterized with the highest values of species abundances.Plant communities were dominated by chamaephytes,anemochorous,arido-active,and competitive stress-tolerant plants.The increase in LSE along the gradient from LLSE to HLSE induced significant changes in plant community variables including decreases in plant density,plant rarity,lifeform composition,morphological type,and aridity adaptation.Desert pavements with HLSE favor the degradation of vegetation and trigger biodiversity erosion.

Fast prediction of the mechanical response for layered pavement under instantaneous large impact based on random forest regression

The layered pavements usually exhibit complicated mechanical properties with the effect of complex material properties under external environment.In some cases,such as launching missiles or rockets,layered pavements are required to bear large impulse load.However,traditional methods cannot non-destructively and quickly detect the internal structural of pavements.Thus,accurate and fast prediction of the mechanical properties of layered pavements is of great importance and necessity.In recent years,machine learning has shown great superiority in solving nonlinear problems.In this work,we present a method of predicting the maximum deflection and damage factor of layered pavements under instantaneous large impact based on random forest regression with the deflection basin parameters obtained from falling weight deflection testing.The regression coefficient R^(2)of testing datasets are above 0.94 in the process of predicting the elastic moduli of structural layers and mechanical responses,which indicates that the prediction results have great consistency with finite element simulation results.This paper provides a novel method for fast and accurate prediction of pavement mechanical responses under instantaneous large impact load using partial structural parameters of pavements,and has application potential in non-destructive evaluation of pavement structure.

Automated Pavement Crack Detection Using Deep Feature Selection and Whale Optimization Algorithm

Pavement crack detection plays a crucial role in ensuring road safety and reducing maintenance expenses.Recent advancements in deep learning(DL)techniques have shown promising results in detecting pavement cracks;however,the selection of relevant features for classification remains challenging.In this study,we propose a new approach for pavement crack detection that integrates deep learning for feature extraction,the whale optimization algorithm(WOA)for feature selection,and random forest(RF)for classification.The performance of the models was evaluated using accuracy,recall,precision,F1 score,and area under the receiver operating characteristic curve(AUC).Our findings reveal that Model 2,which incorporates RF into the ResNet-18 architecture,outperforms baseline Model 1 across all evaluation metrics.Nevertheless,our proposed model,which combines ResNet-18 with both WOA and RF,achieves significantly higher accuracy,recall,precision,and F1 score compared to the other two models.These results underscore the effectiveness of integrating RF and WOA into ResNet-18 for pavement crack detection applications.We applied the proposed approach to a dataset of pavement images,achieving an accuracy of 97.16%and an AUC of 0.984.Our results demonstrate that the proposed approach surpasses existing methods for pavement crack detection,offering a promising solution for the automatic identification of pavement cracks.By leveraging this approach,potential safety hazards can be identified more effectively,enabling timely repairs and maintenance measures.Lastly,the findings of this study also emphasize the potential of integrating RF and WOA with deep learning for pavement crack detection,providing road authorities with the necessary tools to make informed decisions regarding road infrastructure maintenance.

Thermal Properties Reconstruction and Temperature Fields in Asphalt Pavements: Inverse Problem and Optimisation Algorithms

A two-layer implicit difference scheme is employed in the present study to determine the temperature distribution in an asphalt pavement.The calculation of each layer only needs four iterations to achieve convergence.Furthermore,in order to improve the calculation accuracy a swarm intelligence optimization algorithm is also exploited to inversely analyze the laws by which the thermal physical parameters of the asphalt pavement materials change with temperature.Using the basic cuckoo and the gray wolf algorithms,an adaptive hybrid optimization algorithm is obtained and used to determine the relationship between the thermal diffusivity of two types of asphalt pavement materials and the temperature.As shown by the results,the prediction accuracy achievable with this approach is higher than that of the linear model.

Correlation analysis of longitudinal cracks and vertical deformation within asphalt pavement of cold regions

The asphalt pavement longitudinal crack is a common distress in cold regions,resulting from uneven deformation of the subgrade.Analysis of the correlation law between uneven deformation and crack distress is of positive significance for understanding the mechanism of crack initiation,and putting forward treatment measures.In view of the complexity of longitudinal crack inducement and road surface deformation,the grey relational method was used to analyze this relationship.Through long-term monitoring of the vertical deformation data of typical road sections,the vertical deformation law of the pavement surface and its deformation characteristics under the action of temperature field are analyzed.Parameters such as vertical relative deformation,vertical relative deformation rate and vertical differential deformation VDSr were constructed to describe vertical deformation characteristics.Typical distribution characteristics of longitudinal fractures and their length and distribution characteristics are also described.The grey correlation analysis theory was utilized to analyze the relationship between deformation characteristics of sections,cross sections and monitoring points and longitudinal crack characteristics(length and location).The analysis reveals a linear positive correlation or a high correlation between several indicators.This study can provide a deeper understanding of the occurrence and development mechanism of longitudinal cracks in asphalt pavement of cold areas,and give references for the research of road engineering structure,materials and distress prevention.

Improvement of Binocular Reconstruction Algorithm for Measuring 3D Pavement Texture Using a Single Laser Line Scanning Constraint

The dense and accurate measurement of 3D texture is helpful in evaluating the pavement function.To form dense mandatory constraints and improve matching accuracy,the traditional binocular reconstruction technology was improved threefold.First,a single moving laser line was introduced to carry out global scanning constraints on the target,which would well overcome the difficulty of installing and recognizing excessive laser lines.Second,four kinds of improved algorithms,namely,disparity replacement,superposition synthesis,subregion segmentation,and subregion segmentation centroid enhancement,were established based on different constraint mechanism.Last,the improved binocular reconstruction test device was developed to realize the dual functions of 3D texture measurement and precision self-evaluation.Results show that compared with traditional algorithms,the introduction of a single laser line scanning constraint is helpful in improving the measurement’s accuracy.Among various improved algorithms,the improvement effect of the subregion segmentation centroid enhancement method is the best.It has a good effect on both overall measurement and single pointmeasurement,which can be considered to be used in pavement function evaluation.

Texturing and evaluation of concrete pavement surface:A state-of-the-art review

Concrete pavement is accompanied by two major functional properties,namely noise emission and friction,which are closely related to pavement surface texture.While several technologies have been developed to mitigate tirepavement noise and improve driving friction by surface texturization,limited information is available to compare the advantages and disadvantages of different surface textures.In this study,a state-of-the-art and state-of-thepractice review is conducted to investigate the noise reduction and friction improvement technologies for concrete pavement surfaces.The commonly used tests for characterizing the surface texture,skid resistance,and noise emission of concrete pavement were first summarized.Then,the texturing methods for both fresh and hardened concrete pavement surfaces were discussed,and the friction,noise emission and durability performances of various surface textures were compared.It is found that the next generation concrete surface(NGCS)texture generally provides the best noise emission performance and excellent friction properties.The exposed aggregate concrete(EAC)and optimized diamond grinding textures are also promising alternatives.Lastly,the technical parameters for the application of both diamond grinding and diamond grinding&grooving textures were recommended based on the authors'research and practical experience in Germany and the US.This study offers a convenient reference to the pavement researchers and engineers who seek to quickly understand relevant knowledge and choose the most appropriate surface textures for concrete pavements.